|Course number:||MATH-UA 234|
|Time & Location:||Tues & Thurs, 3:30pm - 4:45pm in WWH 512|
|Instructor:||Mike O'Neil (firstname.lastname@example.org)|
|Office hours:||Tues 10:00am - 11:00am & Wed 4:00pm - 5:00pm in WWH 1105A|
|Recitation:||Fri 2:00pm - 3:15pm in WWH 512|
|Teaching assistant:||Sinziana Datcu (email@example.com)|
This course is intended as a thorough mathematical introduction to the theory of statistics, intended to be taken after sufficiency in probability is obtained at the level of Math 233: Theory of Probability. Topics covered in this class will include: sampling theory, hypothesis testing, point (parameter) estimation, regression, tests of significance, likelihood methods, and Bayesian statistics. Topics in computational statistics will be covered using Python and Pandas.
Download a copy of the syllabus here.
The course text is All of Statistics by Larry Wasserman. It can be accessed online through Springer from NYU connected computers at: http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-40272-7
Several other sources might be useful for studying and reference:
The following Python tutorials might be useful:
Important information for the course will appear below as necessary.
Below is a list of homework assignments along with the due date. Remember that each assignment is due at the beginning of class on the due date.